MiroFlow efficiently solves the task scheduling problem through hierarchical intelligent body architecture and modular design:
- Master Intelligence System Coordination Mechanism: Analyze tasks using intent recognition and query augmentation modules for global planning and task scheduling by master intelligences
- Specialized mission assignments: the master intelligence distributes domain-specific tasks to pre-trained sub-intelligences for execution, realizing parallel processing
- MCP Server Integration: Unified scheduling of external tools through the Model Context Protocol Server to avoid resource conflicts
- Fault Tolerance Module: Built-in API flow-limiting response and network retry mechanism to ensure automatic recovery from task interruptions
The scheme achieves a first execution success rate of 72.21 TP3T on the GAIA validation set, proving its scheduling effectiveness.
This answer comes from the articleMiroFlow: a framework for building, managing and scaling AI intelligencesThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of inefficient task scheduling in multi-intelligence systems?
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